no code implementations • 22 Sep 2023 • Jiazhen Wang, Bin Liu, Changtao Miao, Zhiwei Zhao, Wanyi Zhuang, Qi Chu, Nenghai Yu
Existing methods for multi-modal manipulation detection and grounding primarily focus on fusing vision-language features to make predictions, while overlooking the importance of modality-specific features, leading to sub-optimal results.
1 code implementation • 18 May 2023 • Changtao Miao, Qi Chu, Zhentao Tan, Zhenchao Jin, Wanyi Zhuang, Yue Wu, Bin Liu, Honggang Hu, Nenghai Yu
Next, a novel Multi-Spectral Class Center Network (MSCCNet) is proposed for face manipulation detection and localization.
no code implementations • 23 Oct 2022 • Wanyi Zhuang, Qi Chu, Zhentao Tan, Qiankun Liu, Haojie Yuan, Changtao Miao, Zixiang Luo, Nenghai Yu
UPCL is designed for learning the consistency-related representation with progressive optimized pseudo annotations.
no code implementations • 8 Jul 2022 • Wanyi Zhuang, Qi Chu, Haojie Yuan, Changtao Miao, Bin Liu, Nenghai Yu
Existing face forgery detection methods usually treat face forgery detection as a binary classification problem and adopt deep convolution neural networks to learn discriminative features.
1 code implementation • 2 Jun 2021 • Bo Peng, Hongxing Fan, Wei Wang, Jing Dong, Yuezun Li, Siwei Lyu, Qi Li, Zhenan Sun, Han Chen, Baoying Chen, Yanjie Hu, Shenghai Luo, Junrui Huang, Yutong Yao, Boyuan Liu, Hefei Ling, Guosheng Zhang, Zhiliang Xu, Changtao Miao, Changlei Lu, Shan He, Xiaoyan Wu, Wanyi Zhuang
This competition provides a common platform for benchmarking the adversarial game between current state-of-the-art DeepFake creation and detection methods.
no code implementations • 14 Mar 2021 • Changtao Miao, Qi Chu, Weihai Li, Tao Gong, Wanyi Zhuang, Nenghai Yu
Over the past several years, in order to solve the problem of malicious abuse of facial manipulation technology, face manipulation detection technology has obtained considerable attention and achieved remarkable progress.